Quality of Experience (QoE) may be seen as a measure of how satisfied end-users are of a communication service or application running in a communication network. In other words, the QoE defines the quality perceived by the end-users of applications and communication services. Although QoE is historically a subjective value, there exist standardized methods to objectively estimate QoE based on the Quality of Service (QoS) parameters of the network. Indeed, the QoE is, in general, greatly impacted by the quality of the network services, themselves being measured by the QoS parameters. The QoS generally represents the performance of the communication network and the QoS parameters provide an objective measure of the quality of the network's services, in terms of transport of Internet Procotol (IP) packets, for example. In general, five parameters are used to evaluate the QoS of a network, such as bandwidth, delay, jitter, packet loss and availability. Depending on the application, a set of these parameters will have specific threshold values that need to be met in order to ensure an acceptable quality for the end-users.
Many QoE measurement techniques exist for voice, video, real-time and web applications, etc. Most objective QoE calculation methods are derived from QoS parameters. The QoE is usually a byproduct of the QoS, meaning that one way to change the QoE is to change (or enhance) the QoS. For example, in order to enhance the QoE, network operators usually concentrate on the QoS, which is easier to monitor and control. Typical QoS enhancing mechanisms are either traffic conditioning or related to traffic queuing and scheduling. Traffic conditioning includes metering, classification, marking, shaping and policing. Queuing and scheduling algorithms include round-robin, first in first out, priority queuing, etc.
In current systems, no efficient methods are available to dynamically enhance the QoS for a given data flow in response to QoE. Usually, the same QoS is requested for all the data flows belonging to a given QoS class. However, the different data flows in the same QoS class experience different source-destination paths and thus have a different QoE perceived by the end-users. This is a problem that could, to the least, lead to resource wastage (e.g. QoS overprovisioning), because not all the data flows in the given QoS class need the same level of QoS in order to achieve an acceptable QoE or the same level of QoE.
In N. T. Moura, B. A. Vianna, C. V. N. Albuquerque, V. E. F. Rebello, and C. Boeres, “MOS-based rate adaption for VoIP sources” (in ICC'07, IEEE International Conference on Communications, pages 628-633, June 2007), and Q. Zizhi, S. Lingfen, N. Heilemann, and E. Ifeachor: “A new method for VoIP quality of service control use combined adapative sender rate and priority making” (in ICC'04, IEEE International Conference on Communications, 3:1473-1477, June 2004), a method for QoS control using a QoE feedback, which is used to vary the codec's bitrate in a voice call, is disclosed. The QoE information is used by the end-systems.
In the Real-Time Transport Protocol (RTP), the RTP control protocol (RTCP) is an application layer protocol which controls the RTP streams. It periodically transmits information about the characteristics of an RTP session and the QoS. This protocol uses out-of-band signaling, which brings scalability and synchronization challenges on the current networks.
The above solutions for providing a QoE feedback are for layers above the IP layer, such as the application layer. As such, they mostly are usable by end-nodes only.
Therefore, there is a need to better correlate QoS with QoE for a given data or application flow.